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Machine Learning in Complex Networks

Authors: Christiano Silva, Thiago, Zhao, Liang

  • This book combines two important and popular research areas: complex networks and machine learning
  • This book contains not only fundamental background, but also recent research results
  • Numerous illustrative figures and step-by-step examples help readers to understand the main idea and implementation details
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eBook $99.00
price for USA (gross)
  • ISBN 978-3-319-17290-3
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  • Included format: EPUB, PDF
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  • Immediate eBook download after purchase
Hardcover $129.00
price for USA
  • ISBN 978-3-319-17289-7
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
About this book

This book presents the features and advantages offered by complex networks in the machine learning domain. In the first part, an overview on complex networks and network-based machine learning is presented, offering necessary background material. In the second part, we describe in details some specific techniques based on complex networks for supervised, non-supervised, and semi-supervised learning. Particularly, a stochastic particle competition technique for both non-supervised and semi-supervised learning using a stochastic nonlinear dynamical system is described in details. Moreover, an analytical analysis is supplied, which enables one to predict the behavior of the proposed technique. In addition, data reliability issues are explored in semi-supervised learning. Such matter has practical importance and is not often found in the literature. With the goal of validating these techniques for solving real problems, simulations on broadly accepted databases are conducted. Still in this book, we present a hybrid supervised classification technique that combines both low and high orders of learning. The low level term can be implemented by any classification technique, while the high level term is realized by the extraction of features of the underlying network constructed from the input data. Thus, the former classifies the test instances by their physical features, while the latter measures the compliance of the test instances with the pattern formation of the data. We show that the high level technique can realize classification according to the semantic meaning of the data. This book intends to combine two widely studied research areas, machine learning and complex networks, which in turn will generate broad interests to scientific community, mainly to computer science and engineering areas.

Reviews

“The book explores the combined area of complex network-based machine learning. It presents the theoretical concepts underlying the two complementary parts as well as those related to their interaction with respect to supervised, unsupervised and semi-supervised learning. The latest developments in the field, together with real-world test scenarios, are additionally treated in detail.” (Catalin Stoean, zbMATH 1357.68003, 2017)


Table of contents (10 chapters)

  • Introduction

    Silva, Thiago Christiano (et al.)

    Pages 1-13

  • Complex Networks

    Silva, Thiago Christiano (et al.)

    Pages 15-70

  • Machine Learning

    Silva, Thiago Christiano (et al.)

    Pages 71-91

  • Network Construction Techniques

    Silva, Thiago Christiano (et al.)

    Pages 93-132

  • Network-Based Supervised Learning

    Silva, Thiago Christiano (et al.)

    Pages 133-141

Buy this book

eBook $99.00
price for USA (gross)
  • ISBN 978-3-319-17290-3
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $129.00
price for USA
  • ISBN 978-3-319-17289-7
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Machine Learning in Complex Networks
Authors
Copyright
2016
Publisher
Springer International Publishing
Copyright Holder
Springer International Publishing Switzerland
eBook ISBN
978-3-319-17290-3
DOI
10.1007/978-3-319-17290-3
Hardcover ISBN
978-3-319-17289-7
Edition Number
1
Number of Pages
XVIII, 331
Number of Illustrations and Tables
7 b/w illustrations, 80 illustrations in colour
Topics